An algorithm derived from thalamocortical circuitry stores and retrieves temporal sequences

Proceedings - International Conference on Pattern Recognition

Volumn 4, Issue , 1996, Pages 550-554

  Aleksandrovsky, Boris ^a   Whitson, James ^a   Garzotto, Andreas ^a   Lynch, Gary ^a   Granger, Richard ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION; SOFTWARE ENGINEERING;

FEEDBACK CONNECTION; HAND-WRITTEN TEXT RECOGNITION; HANDWRITING RECOGNITION; LONG-TERM POTENTIATIONS; SIZE NORMALIZATION; SPACE AND TIME COMPLEXITY; TEMPORAL FEATURES; TEMPORAL SEQUENCES;

CHARACTER RECOGNITION;

EID: 0011032431     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.1996.547625     Document Type: Conference Paper

References (17)

1. B. Aleksandrovsky, J. Whitson, G. Andes, G.Lynch, and R.Granger. Novel speech processing mechanism derived from auditory neocortical circuit analysis. Proceedings of ICSLP, October 1996.
2. M. Altuwaijri, R. Ayoubi, and M. Bayoumi. Skeletonization of arabic characters using a neural network mapped on maspar. Proceedings of WCNN, 2:653-658, 1994.
3. J. Ambros-Ingerson, R. Granger, and G. Lynch. Simulation of paleocortex performs hierarchical clustering. Science, 247:1344-1348, 1990.
4. T. Bliss and T. Lomo. Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. JournalofPhysiology, 232:331-356, 1973.
5. R. Coultrip and R. Granger. Sparse random networks with LTP leaming rules approximate Bayes classifiers via Parzen's method. Neural Networks, 7:463-476, 1994.
6. A. Garzotto, B. Aleksandrovsky, and R. Granger. A neocortically-derived model of continuous contextual processing. 1996. (in preparation).
7. M. Gluck and R. Granger. Computational models of the neural bases of learning and memory. Annual Review of Neuroscience, 16 667-706, 1993.
8. V. Govindaraju and S. Srihari. Separating handwritten text from interfering strokes. From Pixels to Features III: Frontiers in Handwriting Recognition, 1990.
9. R. Granger, J. Whitson, J. Larson, and G. Lynch. Nonhebbian properties of long-term potentiation enable highcapacity encoding of temporal sequences. Proc. Nat'l. Acad. Sci., 91 : 10104-10108, 1994.
10. K. Kilbom, R. Granger, and G. Lynch. Effects of LTP on response selectivity of simulated cortical neurons. J. Cognitive Neuroscience, 1996. (in press).
11. K. Koffka. Principles of Gestalt Psychology. Harcourt Brace Jovanovitch, New York, 1935.
12. J. Larson and G. Lynch. Theta pattern stimulation and the induction of LTP: The sequence in which synapses are stimulated determines the degree to which they potentiate. Bruin Research, 489:49-58, 1989.
13. J. Larson, D. Wong, and G. Lynch. Patterned stimulation at the theta frequency is optimal for induction of long-term potentiation. Brain Research, 386:347-350, 1986.
14. A. Rockel, R. Hioms, and T. Powell. The basic uniformity in the structure of the neocortex. Brain, 103(2), June 1980.
15. J. Whitson, B. Aleksandrovsky, G.Lynch, and R. Granger. A computational model based upon thalamocortical properties yields a novel circuit that stores and retrieves sequential memories. Cognitive Neuroscience Society, April 1996.
16. J. Whitson, B. Aleksandrovsky, E. Whelpley, G. Lynch, and R. Granger. Searching for a general cortical memory circuit: A computational model based upon cortical homologies stores, retrieves, and recognizes feature sequences. J. Cognitive Neuroscience, 1996. (submitted).
17. M. Wright, R. Cipolla, and P. Giblin. Skeletonization using an extended euclidean distance transform. Image and Vision Computing, 13:367-375, 1995.